Provides a method of recovering the precision matrix for Gaussian graphical models efficiently. Our approach could be divided into three categories. First of all, we use Hard Graphical Thresholding for best subset selection problem of Gaussian graphical model, and the core concept of this method was proposed by Luo et al. (2014) <doi:10.48550/arXiv.1407.7819>. Secondly, a closed form solution for graphical lasso under acyclic graph structure is implemented in our package (Fattahi and Sojoudi (2019) <https://jmlr.org/papers/v20/17-501.html>). Furthermore, we implement block coordinate descent algorithm to efficiently solve the covariance selection problem (Dempster (1972) <doi:10.2307/2528966>). Our package is computationally efficient and can solve ultra-high-dimensional problems, e.g. p > 10,000, in a few minutes.
Version: 0.1.1 Depends: R (≥ 3.2) Imports: Rcpp (≥ 0.12.15), MASS, Matrix LinkingTo: Rcpp, RcppEigen Suggests: testthat, knitr, rmarkdown Published: 2024-01-12 DOI: 10.32614/CRAN.package.gif Author: Shiyun Lin [aut, cre], Jin Zhu [aut], Junxian Zhu [aut], Xueqin Wang [aut], SC2S2 [cph] Maintainer: Shiyun Lin <linshy27 at mail2.sysu.edu.cn> License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] NeedsCompilation: yes Materials: README NEWS CRAN checks: gif results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=gif to link to this page.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4